Service providers and device manufacturers (e.g., wireless, cellular, etc.) are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One area of development has been the creation of large stores or databases of information for use or access through such services.
In addition to there being a significant increase in the number of devices in the market, internet services are leading to vast amounts of data which needs to be managed (both structured and binary) in nature. This data needs to be stored, managed, searched and analyzed. Over the last decade it has been estimated that internet services will have accumulated around 2500 exabytes of data. However, most of this data is not structured in nature; but typically needs to be stored, searched and analyzed appropriately to be useful to users in a meaningful manner in real time.
For example, a mapping service or application may rely on data stores containing millions or even trillions of data records containing information on map features such as points-of-interests, topography, terrain features, and the like. However, as the number of data records increase, service providers and device manufacturers face significant technical challenges to enabling efficient access and query of large information or data systems.
A significant technical issue for distributed storage system design typically involved efficiently accessing geographic information, such as electronic maps, and provides information back to a nomadic device for storage in an energy efficient manner. Since electronic map design typically can be based on an R-Tree implementation, storage systems nowadays are shifting to flash based devices, but there are significant challenges as to how to achieve scalable and energy efficient data translation layers within distributed systems.